Submitted by : Abdul Batish, MOSIP
The current approach involves comparing specific demographic data (Name, Date of Birth, and Gender) of a resident with the existing data stored in the MOSIP system.
- Limited Data Points: The current approach relies primarily on a limited set of demographic data (Name, Date of Birth, and Gender) for deduplication. This may not be sufficient to prevent false positives, especially in cases where individuals have similar demographic information.
- False Negatives: The filtering of potential matches based on registration status can lead to false negatives. For example, Person C, who’s UIN hasn't been generated yet, may be a genuine match but is excluded from consideration.
- Lack of Advanced Techniques: The current approach doesn't utilize advanced techniques like machine learning to improve the accuracy of matching.
To overcome these limitations and enhance deduplication accuracy, an enhanced demographic deduplication engine should be considered, as proposed earlier, incorporating machine learning, advanced algorithms, and more data points for matching.
- Incorporation of Additional Data: We expect participants to develop a deduplication solution that leverages other available information collected during registration. This includes, but is not limited to, demographic data (Name, Date of Birth, Gender), and any other relevant data points that can enhance matching accuracy.
- Flexible Logic with Fuzzy Matching: Participants should design the system with flexible logic, such as fuzzy logic, to enable probability-based matching rather than simple binary (yes/no) matching. This flexibility will improve the system's ability to accommodate variations and similarities in data, reducing both false positives and false negatives.
- Handling Spelling Differences: The solution should consider and accommodate spelling differences in names and other textual data. Implement algorithms or techniques that can identify potential matches even when there are minor variations or misspellings in the data.
- Configurability: We expect the solution to be highly configurable. Users should have the ability to select which data points are used for deduplication and which are not. Configurable options should include the ability to adjust the threshold for considering a match, allowing users to customize the system to their specific needs.
- Scalability: The solution should be designed with scalability in mind, capable of handling a growing number of records and users efficiently. This ensures that it remains effective as the system expands.
- Machine Learning Integration: We encourage the integration of machine learning techniques to continuously improve matching accuracy. Participants should consider implementing algorithms that learn and adapt based on patterns in the data.
- Accuracy and Reliability: The foremost expectation is that the solution significantly improves deduplication accuracy and reliability. It should reduce the chances of both false positives and false negatives, ensuring that the system operates with a high degree of confidence.
- Documentation: Comprehensive documentation should accompany the solution, explaining how it works, how to configure it, and how to interpret results.